BEA528 Open Shop Scheduling Problems

• Learning outcome

  After completing the course, the candidates can

  Knowledge

  • gain a deeper understanding of scheduling problems and the algorithms used to find either exact solutions or efficient approximations

  Skills

  • build a strong foundation in this sub-field to develop and propose a solution approach as part of the PhD thesis.
  • enhance computational skills necessary for implementing the proposed solution.

• Teaching

  Reading course with regular meetings with instructor

• Restricted access

  PhD candidates at NHH only

• Recommended prerequisites

  Advanced knowledge on operations management and operations research.

  Previous knowledge on machine learning and meta-heuristics

• Assessment

  Write a term paper exploring methodologies for solving Open Shop Scheduling Problems, focusing on how these can optimize touristic itineraries for cruise visitors in Bergen. The student must propose a solution strategy to address the stated problem and the specific challenges in Bergen.

• Grading Scale

  Pass/fail

• Computer tools

  Python or C++ programming languages

• Literature

  Tsirlin, A., & Balunov, A. (2022). Job-shop scheduling problem and its solution method. Research Square, 1. https://doi.org/10.21203/rs.3.rs-2294412/v1https://doi.org/10.21203/rs.3.rs-2294412/v1

  Woeginger, G. J. (2018). The open shop scheduling problem. 4, 4:1-4:12. https://doi.org/ 10.4230/LIPIcs.STACS.2018.4

  Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Science Direct, 5, 1093-1113. https://doi.org/10.1016/j.%20asej.2014.04.011https://doi.org/10.1016/j. asej.2014.04.011

  Li, W., Ding, Y., Yang, Y., Sherratt, R. S., Park, J. H., & Wang, J. (2020). Parameterized algorithms of fundamental NP-hard problems: A survey. Springer Link, 10. https: //doi.org/10.1186/s13673-020-00226-w

  Kubiak, W. (2022). A book of open shop scheduling: Algorithms, complexity and applications (1st ed.). 325. https://doi.org/10.1007/978-3-030-91025-9https://doi.org/10.1007/978-3-030-91025-9

  Du, P., Liu, N., Zhang, H., & Lu, J. (2021). An improved ant colony optimization based on an adaptive heuristic factor for the traveling salesman problem. Hindawi, 2021. https://doi.org/10.1155/2021/6642009https://doi.org/10.1155/2021/6642009

  Brucker, P., Sotskov, Y. N., & Werner, F. (2007). Complexity of shop-scheduling problems with fixed number of jobs: A survey. ResearchGate, 65, 461-481. https://doi.org/ 10.1007/s00186-006-0127-8